Refining Semantic Similarity of Paraphasias Using a Contextual Language Model.
Purpose: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia’s naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similari...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 66; no. 1; pp. 206 - 221 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Jan2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161407150&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161407150 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Jan2023 vid: 66 iid: 1 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 161407150 10.1044/2022_JSLHR-22-00277 ppf: 206 ppct: 15 formats: fmt: @attributes: type: P size: 1.5MB tig: atl: Refining Semantic Similarity of Paraphasias Using a Contextual Language Model. aug: au: Salem, Alexandra C. Gale, Robert Casilio, Marianne Fleegle, Mikala Fergadiotis, Gerasimos Bedrick, Steven affil: Oregon Health & Science University, Portland. Vanderbilt University Medical Center, Nashville, TN. Portland State University, OR. su: Semantics Phonetics Vocabulary Phonological awareness Natural language processing Amino acid metabolism disorders Aphasia Quality assurance Research funding Algorithms sug: subj: Semantics Phonetics Vocabulary Phonological awareness Natural language processing Amino acid metabolism disorders Aphasia Quality assurance Research funding Algorithms ab: Purpose: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia’s naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similarity to the target) are important for characterizing an individual’s word-finding deficits or anomia. In this study, we applied a modern language model called BERT (Bidirectional Encoder Representations from Transformers) as a semantic classifier and evaluated its performance against ParAlg’s original word2vec model. Method: We used a set of 11,999 paraphasias produced during the Philadelphia Naming Test. We trained ParAlg with word2vec or BERT and compared their performance to humans. Finally, we evaluated BERT’s performance in terms of word-sense selection and conducted an item-level discrepancy analysis to identify which aspects of semantic similarity are most challenging to classify. Results: Compared with word2vec, BERT qualitatively reduced word-sense issues and quantitatively reduced semantic classification errors by almost half. A large percentage of errors were attributable to semantic ambiguity. Of the possible semantic similarity subtypes, responses that were associated with or category coordinates of the intended target were most likely to be misclassified by both models and humans alike. Conclusions: BERT outperforms word2vec as a semantic classifier, partially due to its superior handling of polysemy. This work is an important step for further establishing ParAlg as an accurate assessment tool. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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